Crop variety identification is an essential link in seed detection, phenotype collection and scientific breeding. This paper takes peanut as an example to explore a new method for crop variety identification. Peanut is a crucial oil crop and cash crop. The yield and quality of different peanut varieties are different, so it is necessary to identify and classify different peanut varieties. The traditional image processing method of peanut variety identification needs to extract many features, which has defects such as intense subjectivity and insufficient generalization ability. Based on the deep learning technology, this paper improved the deep convolutional neural network VGG16 and applied the improved VGG16 to the identification and classification task of 12 varieties of peanuts. Firstly, the peanut pod images of 12 varieties obtained by the scanner were preprocessed with gray-scale, binarization, and ROI extraction to form a peanut pod data set with a total of 3365 images of 12 varieties. A series of improvements have been made to VGG16. Remove the F6 and F7 fully connected layers of VGG16. Add Conv6 and Global Average Pooling Layer. The three convolutional layers of conv5 have changed into Depth Concatenation and add the Batch Normalization(BN) layers to the model. Besides, fine-tuning is carried out based on the improved VGG16. We adjusted the location of the BN layers. Adjust the number of filters for Conv6. Finally, the improved VGG16 model's training test results were compared with the other classic models, AlexNet, VGG16, GoogLeNet, ResNet18, ResNet50, SqueezeNet, DenseNet201 and MobileNetv2 verify its superiority. The average accuracy of the improved VGG16 model on the peanut pods test set was 96.7%, which was 8.9% higher than that of VGG16, and 1.6–12.3% higher than that of other classical models. Besides, supplementary experiments were carried out to prove the robustness and generality of the improved VGG16. The improved VGG16 was applied to the identification and classification of seven corn grain varieties with the same method and an average accuracy of 90.1% was achieved. The experimental results show that the improved VGG16 proposed in this paper can identify and classify peanut pods of different varieties, proving the feasibility of a convolutional neural network in variety identification and classification. The model proposed in this experiment has a positive significance for exploring other Crop variety identification and classification.

Freshness is the most critical indicator for fruit quality, and directly impacts consumers’ physical health and their desire to buy. Also, it is an essential factor of the price in the market. Therefore, it is urgent to study the evaluation method of fruit freshness. Taking banana as an example, in this study, we analyzed the freshness changing process using transfer learning and established the relationship between freshness and storage dates. Features of banana images were automatically extracted using the GoogLeNet model, and then classified by the classifier module. The results show that the model can detect the freshness of banana and the accuracy is 98.92%, which is higher than the human detecting level. In order to study the robustness of the model, we also used this model to detect the changing process of strawberry and found that it is still useful. According to the above results, transfer learning is an accurate, non-destructive, and automated fruit freshness monitoring technique. It may be further applied to the field of vegetable detection.

Ammonia can be produced by the respiration and excretion of fish during the farming process, which can affect the life of fish. In this paper, to research the behavior of fish under different ammonia concentration and make the corresponding judgment and early warning for the abnormal behavior of fish, the different ammonia environments are simulated by adding the ammonium chloride into the water. Different from the existing methods of directly artificial observation or artificial marking, this paper proposed a recognition and analysis of behavior trajectory approach based on deep learning. Firstly, the three-dimensional spatial trajectories of fish are drawn by three-dimensional reconstruction. Then, the influence of different concentrations of ammonia on fish is analyzed according to the behavior trajectory of fish in different concentrations of ammonia. The results of comparative experiments show that the movement of fish and vitality decrease significantly, and the fish often stagnates in the water of containing ammonium chloride. The proposed approach can provide a new idea for the behavior analysis of animal.

The motion trajectory of sea cucumbers reflects the behavior of sea cucumbers, and the behavior of sea cucumbers reflects the status of the feeding and individual health, which provides the important information for the culture, status detection and early disease warning. Different from the traditional manual observation and sensor-based automatic detection methods, this paper proposes a detection, location and analysis approach of behavior trajectory based on Faster R-CNN for sea cucumbers under the deep learning framework. The designed detection system consists of a RGB camera to collect the sea cucumbers' images and a corresponding sea cucumber identification software. The experimental results show that the proposed approach can accurately detect and locate sea cucumbers. According to the experimental results, the following conclusions are drawn: (1) Sea cucumbers have an adaptation time for the new environment. When sea cucumbers enter a new environment, the adaptation time is about 30 minutes. Sea cucumbers hardly move within 30 minutes and begin to move after about 30 minutes. (2) Sea cucumbers have the negative phototaxis and prefers to move in the shadows. (3) Sea cucumbers have a tendency to the edge. They like to move along the edge of the aquarium. When the sea cucumber is in the middle of the aquarium, the sea cucumber will look for the edge of the aquarium. (4) Sea cucumbers have unidirectional topotaxis. They move along the same direction with the initial motion direction. The proposed approach will be extended to the detection and behavioral analysis of the other marine organisms in the marine ranching.